Field of the Art
The disclosure relates to the field of linear feature extraction, particularly from remotely-sensed raster data.
Discussion of the State of the Art
In the art of linear feature extraction, ROADTRACKER™ and similar tools enable automated bulk extraction and semi-automated point-to-point extraction of two-dimensional linear feature vectors from remotely-sensed imagery. The extracted vectors represent centerlines of linear features within the image raster. Extractions by these tools are image-based, meaning the image content automatically drives the shapes of extracted vectors. In semi-automated extraction, the raster is displayed in a viewer and extraction is partially guided by user mouse clicks placed along a desired linear feature. Tools like the ROADTRACKER™ can be used to extract centerlines for roads, trails, and hydrology features, and includes automatic smoothing of the vectors and automatic topology cleaning (elimination of gaps (under-shoots) and dangles (over-shoots) where vectors are intended to be perfectly incident to one another.) There are, however, several shortcomings to these tools. One is that although the geometric accuracy of the automated bulk extraction is usually good enough for isolated roads and curved roads, it is often not satisfactory for rectangular city road grids because the extracted centerlines often are not as straight, parallel, or evenly-positioned as desired. Another deficiency is that the tools do not provide any capability for three-dimensional linear feature extraction. And finally, when the semi-automated extraction of a linear feature involves a sequence of more than two mouse clicks, extraction does not commence until placement of the last mouse click. A more preferred behavior would be for the extraction to grow incrementally each time a new mouse click is added to the sequence.
What is needed are the following: a more accurate two-dimensional automated bulk extraction capability to capture rectangular city road grids; a three-dimensional automated and semi-automated linear feature extraction capability that utilizes a digital surface model (DSM) and performs automatic vector smoothing and automatic topology cleaning; a three-dimensional automated and semi-automated linear feature vector extraction capability that utilizes high-resolution stereo imagery and performs automatic vector smoothing and automatic topology cleaning; and finally, whether performing two-dimensional or three-dimensional semi-automated image-based linear feature vector extraction, when the feature extraction involves a sequence of more than two mouse clicks, the extracted vector should grow incrementally each time a new mouse click is added to the sequence.